import math
import torch
from torch import nn
from d2l import torch as d2l

def masked_softmax(X, valid_lens):
    """通过在最后一个轴上掩蔽元素来执行softmax操作"""
    # X:3D张量,valid_lens:1D或2D张量
    if valid_lens is None:
        return nn.functional.softmax(X, dim=-1)
    else:
        shape = X.shape
        if valid_lens.dim() == 1:
            valid_lens = torch.repeat_interleave(
                valid_lens, shape[1])
        else:
            valid_lens = valid_lens.reshape(-1)
            # 最后一轴上被掩蔽的元素使用一个非常大的负值替换,
            #从而其softmax输出为0
        X = d2l.sequence_mask(
            X.reshape(-1, shape[-1]), valid_lens, value=-1e6)
        return nn.functional.softmax(
            X.reshape(shape), dim=-1)

class DotProductAttention(nn.Module):
    """缩放点积注意力"""
    def __init__(self, dropout, **kwargs):
        super(DotProductAttention, self).__init__(**kwargs)
        self.dropout = nn.Dropout(dropout)
        # queries的形状:(batch_size,查询的个数,d)
        # keys的形状:(batch_size, “键-值”对的个数,d)
        # values的形状:(batch_size, “键-值”对的个数,值的维度)
        # valid_lens的形状:(batch_size,)或者(batch_size,查询的个数)
    def forward(self, queries, keys, values, valid_lens=None):
        d = queries.shape[-1]
        # 设置transpose_b=True为了交换keys的最后两个维度
        scores = torch.bmm(queries, keys.transpose(1,2)) / math.sqrt(d)
        self.attention_weights = masked_softmax(scores, valid_lens)
        return torch.bmm(self.dropout(self.attention_weights), values)

class AdditiveAttention(nn.Module):
    """加性注意力"""
    def __init__(self, key_size, query_size, num_hiddens, dropout, **kwargs):
        super(AdditiveAttention, self).__init__(**kwargs)
        self.W_k = nn.Linear(key_size, num_hiddens, bias=False)
        self.W_q = nn.Linear(query_size, num_hiddens, bias=False)
        self.w_v = nn.Linear(num_hiddens, 1, bias=False)
        self.dropout = nn.Dropout(dropout)
    def forward(self, queries, keys, values, valid_lens):
        queries, keys = self.W_q(queries), self.W_k(keys)
        # 在维度扩展后,
        # queries的形状:(batch_size,查询的个数,1,num_hidden)
        # key的形状:(batch_size,1, “键-值”对的个数,num_hiddens)
        # 使用广播方式进行求和
        features = queries.unsqueeze(2) + keys.unsqueeze(1)
        features = torch.tanh(features)
        # self.w_v仅有一个输出,因此从形状中移除最后那个维度。
        # scores的形状:(batch_size,查询的个数, “键-值”对的个数)
        scores = self.w_v(features).squeeze(-1)
        self.attention_weights = masked_softmax(scores, valid_lens)
        # values的形状:(batch_size, “键-值”对的个数,值的维度)
        return torch.bmm(self.dropout(self.attention_weights), values)

queries, keys = torch.normal(0, 1, (2, 1, 20)), torch.normal(0,1,(2, 10, 2))
# values的小批量,两个值矩阵是相同的
values = torch.arange(40, dtype=torch.float32).reshape(1, 10, 4).repeat( 2, 1, 1)
valid_lens = torch.tensor([2, 6])
attention = AdditiveAttention(key_size=2, query_size=20, num_hiddens=8, dropout=0.1)
attention.eval()
print(attention(queries, keys, values, valid_lens))